code: def _get_max_value(dataset, feature_key):
      list_scalars = np.concatenate([g[feature_key] for g in dataset])
      return np.max(list_scalars)
  
imports: import logging
  import networkx
  import numpy
  import torch
  import torch.nn
  from deepsnap.graph import Graph
  import graphgym.register
  from graphgym.config import cfg
  from graphgym.contrib.transform.identity import compute_identity
label_desc: Use image node_id 7 for calling the FeatureAugment obj's underlying member method code with example usage: obj._get_max_value(dataset, feature_key) and returns: np